Dataproc FAQ


What is Dataproc?

Dataproc is a fast, easy-to-use, low-cost and fully managed service that lets you run the Apache Spark and Apache Hadoop ecosystem on Google Cloud Platform. Dataproc provisions big or small clusters rapidly, supports many popular job types, and is integrated with other Google Cloud Platform services, such as Cloud Storage and Cloud Logging, thus helping you reduce TCO.

How is Dataproc different from traditional Hadoop clusters?

Dataproc is a managed Spark/Hadoop service intended to make Spark and Hadoop easy, fast, and powerful. In a traditional Hadoop deployment, even one that is cloud-based, you must install, configure, administer, and orchestrate work on the cluster. By contrast, Dataproc handles cluster creation, management, monitoring, and job orchestration for you.

How can I use Dataproc?

There are a number of ways you can use a Dataproc cluster depending on your needs and capabilities. You can use the browser-based Google Cloud console to interact with Dataproc. Or, because Dataproc is integrated with the Google Cloud CLI, you can use the Google Cloud CLI. For programmatic access to clusters, use the Dataproc REST API. You can also make SSH connections to master or worker nodes in your cluster.

How does Dataproc work?

Dataproc is a managed framework that runs on the Google Cloud Platform and ties together several popular tools for processing data, including Apache Hadoop, Spark, Hive, and Pig. Dataproc has a set of control and integration mechanisms that coordinate the lifecycle, management, and coordination of clusters. Dataproc is integrated with the YARN application manager to make managing and using your clusters easier.

What type of jobs can I run?

Dataproc provides out-of-the box and end-to-end support for many of the most popular job types, including Spark, Spark SQL, PySpark, MapReduce, Hive, and Pig jobs.

What Cluster Manager does Dataproc use with Spark?

Dataproc runs Spark on YARN.

How frequently are the components in Dataproc updated?

Dataproc is updated when major releases occur in underlying components (Hadoop, Spark, Hive, Pig). Each major Dataproc release supports specific versions of each component (see Supported Dataproc versions).

Is Dataproc integrated with other Google Cloud Platform products?

Yes, Dataproc has native and automatic integrations with Compute Engine, Cloud Storage, Bigtable, BigQuery, Logging, and Cloud Monitoring. Moreover, Dataproc is integrated into tools that interact with the Cloud Platform including the gcloud CLI and the Google Cloud console.

Can I run a persistent cluster?

Once started, Dataproc clusters continue to run until shut down. You can run a Dataproc cluster for as long as you need.

Cluster management

Can I run more than one cluster at a time?

Yes, you can run more than one Dataproc cluster per project simultaneously. By default, all projects are subject to Google Cloud resource quotas. You can easily check your quota usage and request an increase to your quota. For more information, see Dataproc resource quotas.

How can I create or destroy a cluster?

You can create and destroy clusters in several ways. The Dataproc sections in the Google Cloud console make it easy to manage clusters from your browser. Clusters can also be managed via the command line through the gcloud CLI. For more complex or advanced use cases, the Cloud Dataproc REST API can be used to programmatically manage clusters.

Can I apply customized settings when I create a cluster?

Dataproc supports initialization actions that are executed when a cluster is created. These initialization actions can be scripts or executables that Dataproc will run when provisioning your cluster to customize settings, install applications, or make other modifications to your cluster.

How do I size a cluster for my needs?

Cluster sizing decisions are influenced by several factors, including the type of work to be performed, cost constraints, speed requirements, and your resource quota. Since Dataproc can be deployed on a variety of machine types, you have the flexibility to choose the resources you need, when you need them.

Can I resize my cluster?

Yes, you can easily resize your cluster, even during job processing. You can resize your cluster through the Google Cloud console or through the command line. Resizing can increase or decrease the number of workers in a cluster. Workers added to a cluster will be the same type and size as existing workers. Resizing clusters is acceptable and supported except in special cases, such as reducing the number of workers to one or reducing HDFS capacity below the amount needed for job completion.

Job and workflow management

How can I submit jobs on my cluster?

There are several ways to submit jobs on a Dataproc cluster. The easiest way is to use the Dataproc Submit a job page on the Google Cloud console or the gcloud CLI gcloud dataproc jobs submit command. For programmatic job submission, see the Dataproc API reference.

Can I run more than one job at a time?

Yes, you can run more than one job at a time on a Dataproc cluster. Cloud Dataproc utilizes a resource manager (YARN) and application-specific configurations, such as scaling with Spark, to optimize the use of resources on a cluster. Job performance will scale with cluster size and the number of active jobs.

Can I cancel jobs on my cluster?

Definitely. Jobs can be canceled via the Google Cloud console web interface or the command line. Dataproc utilizes YARN application cancellation to stop jobs upon request.

Can I automate jobs on my cluster?

Jobs can be automated to run on clusters through several mechanisms. You can use the gcloud CLI Google Cloud CLI or the Dataproc REST APIs to automate the management and workflow of clusters and jobs.


What development languages are supported?

You can use languages supported by the Spark/Hadoop ecosystem, including Java, Scala, Python, and R.

Does Dataproc have an API?

Yes, Dataproc has a set of RESTful APIs that allow you to programmatically interact with clusters and jobs.

Can I SSH into a cluster?

Yes, you can SSH into every machine (master or worker node) within a cluster. You can SSH from a browser or from the command line.

Can I access the Spark/Hadoop Web UIs?

Yes, the Hadoop and Spark UIs (Spark, Hadoop, YARN UIs) are accessible within a cluster. Rather than opening ports for the UIs, we recommend using an SSH tunnel, which will securely forward traffic from clusters over the SSH connection.

Can I install or manage software on my cluster?

Yes, as with a Hadoop cluster or server, you can install and manage software on a Dataproc cluster.

What is the default replication factor?

Due to performance considerations as well as the high reliability of storage attached to Dataproc clusters, the default replication factor is set at 2.

What operating system (OS) is used for Dataproc?

Dataproc is based on Debian and Ubuntu. The latest images are based on Debian 10 Buster and Ubuntu 18.04 LTS.

Where can I learn about Hadoop streaming?

You can review the Apache project documentation.

How do I install the gcloud dataproc command?

When you install the gcloud CLI, the standard gcloud command-line tool is installed, including gcloud dataproc commands.

Data access & availability

How can I get data in and out of a cluster?

Dataproc utilizes the Hadoop Distributed File System (HDFS) for storage. Additionally, Dataproc automatically installs the HDFS-compatible Google Cloud Storage connector, which enables the use of Cloud Storage in parallel with HDFS. Data can be moved in and out of a cluster through upload/download to HDFS or Cloud Storage.

Can I use Cloud Storage with Dataproc?

Yes, Dataproc clusters automatically install the Cloud Storage connector. There are a number of benefits to choosing Cloud Storage over traditional HDFS including data persistence, reliability, and performance.

Can I get Cloud Storage Connector support?

Yes, when used with Dataproc, the Cloud Storage connector is supported at the same level as Dataproc (see Get support). All connector users can use the google-cloud-dataproc tag on Stack Overflow for connector questions and answers.

What's the ideal file size for datasets on HDFS and Cloud Storage?

To improve performance, store data in larger file sizes, for example, file sizes in the 256MB–512MB range.

How reliable is Dataproc?

Because Dataproc is built on reliable and proven Google Cloud Platform technologies, including Compute Engine, Cloud Storage, and Monitoring, it is designed for high availability and reliability. As a generally available product, you can review the Dataproc SLA.

What happens to my data when a cluster is shut down?

Any data in Cloud Storage persists after your cluster is shut down. This is one of the reasons to choose Cloud Storage over HDFS since HDFS data is removed when a cluster is shut down (unless it is transferred to a persistent location prior to shutdown).

Logging, monitoring, & debugging

What sort of logging and monitoring is available?

By default, Dataproc clusters are integrated with Monitoring and Logging. Monitoring and Logging make it easy to get detailed information about the health, performance, and status of your Dataproc clusters. Both application (YARN, Spark, etc.) and system logs are forwarded to Logging.

How can I view logs from Dataproc?

You can view logs from Dataproc in several ways. You can visit Logging to view aggregated cluster logs in a web browser. Additionally, you can use the command-line (SSH) to manually view logs or monitor application outputs. Finally, details are also available via the Hadoop application web UIs, such as the YARN web interface.

How can clusters be monitored?

Clusters can be easily monitored through Monitoring or the Cloud Dataproc section of the Google Cloud console. You can also monitor your clusters through command-line (SSH) access or the application (Spark, YARN, etc.) web interfaces.

Security & access

How is my data secured?

Google Cloud Platform employs a rich security model, which also applies to Cloud Dataproc. Dataproc provides authentication, authorization, and encryption mechanisms, such as SSL, to secure data. Data can be user encrypted in transit to and from a cluster, upon cluster creation or job submission.

How can I control access to my Dataproc cluster?

Google Cloud Platform offers authentication mechanisms, which can be used with Dataproc. Access to Dataproc clusters and jobs can be granted to users at the project level.


How is Dataproc billed?

Dataproc is billed by the second, and is based on the size of a cluster and the length of time the cluster is operational. In computing the cluster component of the fee, Dataproc charges a flat fee based on the number of virtual CPUs (vCPUs) in a cluster. This flat fee is the same regardless of the machine type or size of the Compute Engine resources used.

Am I charged for other Google Cloud resources?

Yes, running a Dataproc cluster incurs charges for other Google Cloud resources used in the cluster, such as Compute Engine and Cloud Storage. Each item is stated separately in your bill, so you know exactly how your costs are calculated and allocated.

Is there a minimum or maximum time for billing?

Google Cloud charges are calculated by the second, not by the hour. Currently, Compute Engine has a 1-minute minimum billing increment. Therefore, Dataproc also has a 1-minute minimum billing increment.


Who can create a Dataproc cluster?

Dataproc is generally available which means all Google Cloud Platform customers can use it.

In which regions is Dataproc available?

Dataproc is available across all regions and zones of the Google Cloud platform.